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1.49 kB
| #!/usr/bin/env python | |
| """ | |
| startup_config | |
| Startup configuration utilities | |
| """ | |
| from __future__ import absolute_import | |
| import importlib | |
| import os | |
| import random | |
| import sys | |
| import numpy as np | |
| import torch | |
| __author__ = "Xin Wang" | |
| __email__ = "wangxin@nii.ac.jp" | |
| __copyright__ = "Copyright 2020, Xin Wang" | |
| def set_random_seed(random_seed, args=None): | |
| """set_random_seed(random_seed, args=None) | |
| Set the random_seed for numpy, python, and cudnn | |
| input | |
| ----- | |
| random_seed: integer random seed | |
| args: argue parser | |
| """ | |
| # initialization | |
| torch.manual_seed(random_seed) | |
| random.seed(random_seed) | |
| np.random.seed(random_seed) | |
| os.environ["PYTHONHASHSEED"] = str(random_seed) | |
| # For torch.backends.cudnn.deterministic | |
| # Note: this default configuration may result in RuntimeError | |
| # see https://pytorch.org/docs/stable/notes/randomness.html | |
| if args is None: | |
| cudnn_deterministic = True | |
| cudnn_benchmark = False | |
| else: | |
| cudnn_deterministic = args.cudnn_deterministic_toggle | |
| cudnn_benchmark = args.cudnn_benchmark_toggle | |
| if not cudnn_deterministic: | |
| print("cudnn_deterministic set to False") | |
| if cudnn_benchmark: | |
| print("cudnn_benchmark set to True") | |
| if torch.cuda.is_available(): | |
| torch.cuda.manual_seed_all(random_seed) | |
| torch.backends.cudnn.deterministic = cudnn_deterministic | |
| torch.backends.cudnn.benchmark = cudnn_benchmark | |
| return | |